Scholarlink

<p>Cutaneous squamous cell carcinoma (cSCC) is the most common skin cancer with metastatic potential and development of metastases carries a poor prognosis. To address the need for reliable risk stratification, we developed cSCCNet, a deep learning model using digital pathology of primary cSCC to predict metastatic risk. A retrospective cohort of 227 primary cSCC from four centres is used for model development. cSCCNet automatically selects the tumour area in standard histopathological slides and then stratifies primary cSCC into high- vs. low-risk categories, with heatmaps indicating most predictive tiles contributing to explainability. On a 20% hold-out testing cohort, cSCCNet achieves an area under the curve (AUC) of 0.95 and 95% accuracy in predicting risk of metastasis, outperforming gene expression-based tools and clinicopathologic classifications. Multivariate analysis including common clinicopathologic classifications confirms cSCCNet as an independent predictor for metastasis, implying it identifies predictive features beyond known clinicopathologic risk factors. Histopathological analysis including multiplex immunohistochemistry suggests that tumour differentiation, acantholysis, desmoplasia, and the spatial localisation of lymphocytes relative to tumour tissue may be important in predicting risk of developing metastasis. Although further validation including prospective evaluation is required, cSCCNet has potential as a reliable and accurate tool for metastatic risk prediction that could be easily integrated into existing histopathology workflows.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhanced metastasis risk prediction in cutaneous squamous cell carcinoma using deep learning and computational histopathology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月2日

查看全文
  • Emilia Peleva,
  • Yue Chen,
  • Bernhard Finke,
  • Hasan Rizvi,
  • Eugene Healy,
  • Chester Lai,
  • Paul Craig,
  • William Rickaby,
  • Christina Schoenherr,
  • Craig Nourse,
  • Charlotte Proby,
  • Gareth J. Inman,
  • Irene M. Leigh,
  • Catherine A. Harwood,
  • Jun Wang

摘要

Cutaneous squamous cell carcinoma (cSCC) is the most common skin cancer with metastatic potential and development of metastases carries a poor prognosis. To address the need for reliable risk stratification, we developed cSCCNet, a deep learning model using digital pathology of primary cSCC to predict metastatic risk. A retrospective cohort of 227 primary cSCC from four centres is used for model development. cSCCNet automatically selects the tumour area in standard histopathological slides and then stratifies primary cSCC into high- vs. low-risk categories, with heatmaps indicating most predictive tiles contributing to explainability. On a 20% hold-out testing cohort, cSCCNet achieves an area under the curve (AUC) of 0.95 and 95% accuracy in predicting risk of metastasis, outperforming gene expression-based tools and clinicopathologic classifications. Multivariate analysis including common clinicopathologic classifications confirms cSCCNet as an independent predictor for metastasis, implying it identifies predictive features beyond known clinicopathologic risk factors. Histopathological analysis including multiplex immunohistochemistry suggests that tumour differentiation, acantholysis, desmoplasia, and the spatial localisation of lymphocytes relative to tumour tissue may be important in predicting risk of developing metastasis. Although further validation including prospective evaluation is required, cSCCNet has potential as a reliable and accurate tool for metastatic risk prediction that could be easily integrated into existing histopathology workflows.